In order to better meet the accuracy and timeliness requirements of Chinese food dish recognition， a new type of dish recognition network was designed. The original YOLOv5 model was pruned by combining Supermask method and structured channel pruning method， and lightweighted finally by Int8 quantization technology. This ensured that the proposed model could balance accuracy and speed in dish recognition， achieving a good trade-off while improving the model portability. Experimental results show that the proposed model achieves a mean Average Precision （mAP） of 99.00% and an average recognition speed of 59.54 ms /frame at an Intersection over Union （IoU） of 0.5， which is 20 ms/frame faster than that of the original YOLOv5 model while maintaining the same level of accuracy. In addition， the new dish recognition network was ported to the Renesas RZ/G2L board by Qt. Based on this， an intelligent service system was constructed to realize the whole process of ordering， generating orders， and automatic meal distribution. A theoretical and practical foundation was provided for the future construction and application of truly intelligent service systems in restaurants.

Aiming at the balance optimization problem of Lightweight Convolutional Neural Network （LCNN） in accuracy and complexity， an adaptive multi-scale feature channel grouping optimization algorithm based on fast Non-dominated Sorting Genetic Algorithm （NSGA-Ⅱ） was proposed to optimize the feature channel grouping structure of LCNN. Firstly， the complexity minimization and accuracy maximization of the feature fusion layer structure in LCNN were regarded as two optimization objectives， and the dual-objective function modeling and theoretical analysis were carried out. Then， a LCNN structure optimization framework based on NSGA-Ⅱ was designed， and an adaptive grouping layer based on NSGA-Ⅱ was added to deep convolution layer in original LCNN structure， thus constructing an Adaptive Multi-scale Feature Fusion Network based on NSGA2 （NSGA2-AMFFNetwork）. Experimental results on image classification datasets show that compared with the manually designed network structure M_blockNet_v1， NSGA2-AMFFNetwork has the average accuracy improved by 1.220 2 percentage points， and the running time decreased by 41.07%. This above indicates that the proposed optimization algorithm can balance the complexity and accuracy of LCNN， and also provide more options for network structure with balanced performance for ordinary users who lack domain knowledge.

Aiming at the serious waste of energy in the current lighting environment， a lighting control optimization method based on Progressive Sparrow Search Algorithm （P-SSA） was proposed. Firstly， to increase the diversity of the initial population， avoid premature convergence and enhance the ability to search for optimization， the Logistic chaotic initialization， the Cauchy mutation and the memory function of the historical optimal position were introduced into SSA. Then， the presence of people in the light environment， the distribution of natural light and the coupling between multiple lamps and lanterns were comprehensively considered to establish a fitness function. DIALux evo professional lighting simulation software was used to obtain the artificial illuminance transfer matrix and natural illuminance distribution. Finally， the performance of P-SSA was verified， and several optimization algorithms were used to carry out experiments about optimization of the combination of dimming coefficients. Experimental results show that compared with optimization algorithms such as Particle Swarm Optimization algorithm （PSO） and Arithmetic Optimization Algorithm （AOA）， the lighting control optimization method based on P-SSA can find the combination of optimal dimming coefficients quickly and accurately， and meet the requirement of maximum energy saving under the premise of comfort.